AI-driven Predictive Analytics for Drug Stability Studies

Review Article

Abstract

Drug stability studies play a pivotal role in ensuring the safety, efficacy, and quality of pharmaceutical products throughout their shelf life. The advent of artificial intelligence (AI) and predictive analytics has revolutionized the way these studies are conducted, offering unprecedented opportunities for accurate predictions, cost reduction, and accelerated drug development timelines. This review article explores the application of AI-driven predictive analytics in drug stability studies, highlighting its impact on various aspects of the process. The article delves into the fundamental concepts of predictive analytics and its integration with AI techniques, including machine learning algorithms and deep learning networks. It examines the data sources and preprocessing methods required for building robust predictive models, encompassing physicochemical properties, formulation composition, and environmental factors affecting drug stability. Furthermore, the review discusses the application of AI-driven predictive analytics in various stages of drug stability studies, such as accelerated stability testing, real-time stability monitoring, and shelf-life estimation. It also explores the potential of these techniques in optimizing formulation design, identifying critical quality attributes, and enabling continuous process verification. Additionally, the article addresses the challenges and limitations associated with implementing AI-driven predictive analytics in drug stability studies, including data quality, model interpretability, and regulatory considerations. Finally, it provides insights into future trends and potential areas of research, emphasizing the pivotal role of AI in enhancing drug product quality and patient safety